Makov chain is a powerful tool to study sequential data. It provides a bunch of models to analyze time series data and predict the variation tendencies of random processes, including discrete-time Markov chain(DTMC)[1]}, continuous-time Markov chain[2]}, [3]}, hidden Markov chain[4]}, [5]}, etc. Among these, DTMC model having the properties of both simplicity and effectiveness has a widely application in realistic programs[6]}, [7]}. Classical DTMC does a great job in prediction when the discrete states are easy to distinguish. However, The uncertainty and vagueness exist in real world inevitably. The application of DTMC model is limited when the states are not discrete or the realistic states are uncertain. For example, the states can not be determined according to the known data or the collected data may not be crisp. Fuzzy mathematics is a great tool to handle with uncertainty[8]}, [9]}, [10]}. To address it, some modified models have been proposed like fuzzy Markov chain[11]}, [12]} and fuzzy states based on Markov chain[13]}, [14]}. These models introduce a certain subordinating degree function between the states distribution and the states description. However, considering the complexity of the realistic program, the certain function may be hard to build. Moreover, the evidential Markov chain[15]}, [16]} and some generalized model models[17]}, [18]} are also proposed.
